How Does Mobile Context Affect People's Web Search Behavior?: A Diary Study of Mobile Information Needs and Search Behaviors

2012 ◽  
Author(s):  
Daijiro Komaki ◽  
Takahiro Hara ◽  
Shojiro Nishio
Keyword(s):  
Information Needs ◽  
Web Search ◽  
Search Behavior ◽  
Diary Study ◽  
Mobile Information ◽  
Search Behaviors ◽  
Mobile Context

scholarly journals Web Search Behavior and Information Needs of People With Multiple Sclerosis: Focus Group Study and Analysis of Online Postings

2014 ◽  
Vol 3 (3) ◽  
pp. e12 ◽  
Author(s):  
Cinzia Colombo ◽  
Paola Mosconi ◽  
Paolo Confalonieri ◽  
Isabella Baroni ◽  
Silvia Traversa ◽  
...  
Keyword(s):  
Multiple Sclerosis ◽  
Focus Group ◽  
Information Needs ◽  
Web Search ◽  
Search Behavior ◽  
Focus Group Study

scholarly journals Predicting implicit search behaviors usinglog analysis

2018 ◽  
Vol 7 (1.7) ◽  
pp. 91
Author(s):  
L LeemaPriyadharshini ◽  
S Florence ◽  
K Prema ◽  
C Shyamala Kumari
Keyword(s):  
Web Search ◽  
User Behavior ◽  
Search Behavior ◽  
Web Page ◽  
User Behaviors ◽  
Needed Information ◽  
Efficiency And Effectiveness ◽  
Search Behaviors ◽  
User Search Behavior ◽  
The Web

Search engines provide ranked information based on the query given by the user. Understanding user search behavior is an important task for satisfaction of the users with the needed information. Understanding user search behaviors and recommending more information or more sites to the user is an emerging task. The work is based on the queries given by the user, the amount of time the user spending on the particular page, the number of clicks done by the user particular URL. These details will be available in the dataset of web search log. The web search log is nothing but the log which contains the user searching activities and other details like machine ID, browser ID, timestamp, query given by the user, URL accessed etc., four things considered as the important: 1) Extraction of tasks from the sequence of queries given by the user 2) suggesting some similar query to the user 3) ranking URLs based on the implicit user behaviors 4) increasing web page utilities based on the implicit behaviors. For increasing the web page utility and ranking the URLs predicting implicit user behavior is a needed task. For each of these four things designing and implementation of some algorithms and techniques are needed to increase the efficiency and effectiveness.

scholarly journals A Crowd-Powered Task Generation Method for Study of Struggling Search

2021 ◽  
Author(s):  
Luyan Xu ◽  
Xuan Zhou
Keyword(s):  
Information Needs ◽  
User Behavior ◽  
Search Behavior ◽  
High Quality ◽  
Task Set ◽  
Interactive Search ◽  
Search Tasks ◽  
Search Behaviors ◽  
Task Sets ◽  
Simple Search

AbstractEvaluation of interactive search systems and study of users’ struggling search behaviors require a significant number of search tasks. However, generation of such tasks is inherently difficult, as each task is supposed to trigger struggling search behavior rather than simple search behavior. To the best of our knowledge, there has not been a commonly used task set for research in struggling search. Moreover, the everchanging landscape of information needs would render old task sets less ideal if not unusable for evaluation. To deal with this problem, we propose a crowd-powered task generation method and develop a platform to efficiently generate struggling search tasks on basis of online wikis such as Wikipedia. Our experiments and analysis show that the generated tasks are qualified to emulate struggling search behaviors consisting of “repeated similar queries” and “quick-back clicks”; tasks of diverse topics, high quality and difficulty can be created using this method. For benefit of the community, we publicly released a task generation platform TaskGenie, a task set of 80 topically diverse struggling search tasks with “baselines,” and the corresponding anonymized user behavior logs.

A longitudinal study of information needs and search behaviors in science and technology

2016 ◽  
Vol 34 (1) ◽  
pp. 83-98 ◽  
Author(s):  
Minsoo Park ◽  
Tae-Seok Lee
Keyword(s):  
Information System ◽  
Information Needs ◽  
Web Search ◽  
Science And Technology ◽  
Technical Information ◽  
Academic Disciplines ◽  
User Research ◽  
Content Type ◽  
Long Run ◽  
Search Behaviors

Purpose This study aims at a longitudinal understanding of the user–system interactions from the context of science and technology at a query level. Design/methodology/approach The authors quantitatively analyzed log data sets culled from more than 24,820,416 queries submitted by users of a national scientific and technical information system, collected in 2008-2011. Findings In the fields of science and technology, the user search behaviors and patterns have remained stable. User queries are short and simple. In all, 80 per cent of the queries are made up of one-three terms. The length of query on a scholarly information system in the fields of science and technology is different from that of Web search. The former is longer than the latter. Search topics have shifted fast. “FUEL BATTERY”, “NANO”, “OLED”, “CAR”, “ROBOT” and “SMARTPHONE” were high-ranked queries from 2008 to 2011. It was found that the time to determine whether the users will stay on the site took about 10 seconds on average from the time of visit. If the users viewed the results of a list generated by the search query and took any action, such as detailed view, export or full-text download, most of them stayed more than 10 minutes on average. Originality/value Longitudinal user research using a query analysis helps to understand the information needs and behavioral patterns of users on information systems related to a specific field and those based on the Web. It also brings insights into the past, present and future events of a field. In other words, it plays a role as a mirror that reflects the flow of time. In the long run, it will be an historic asset. In the future, user studies using a query analysis need to be carried out from various (e.g. social, cultural or other academic disciplines) long-term perspectives on a continuous basis.

Classification of means and methods of the Web semantic retrieval

2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  
Keyword(s):  
Search Engines ◽  
Domain Knowledge ◽  
Information Needs ◽  
Web Search ◽  
User Interaction ◽  
Query Languages ◽  
Semantic Search ◽  
Semantic Retrieval ◽  
The Web

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.

The dilemma of the direct answer

2020 ◽  
Vol 54 (1) ◽  
pp. 1-12
Author(s):  
Martin Potthast ◽  
Matthias Hagen ◽  
Benno Stein
Keyword(s):  
Paradigm Shift ◽  
Information Needs ◽  
Web Search ◽  
Information Sources ◽  
Information Access ◽  
User Preferences ◽  
Information System Design ◽  
Trade Offs ◽  
Remarkable Progress ◽  
Direct Answer

No Web technology has undergone such an impressive evolution as Web search engines did and still do. Starting with the promise of "Bringing order to the Web" 1 by compiling information sources matching a query, retrieval technology has been evolving to a kind of "oracle machinery", being able to recommend a single source, and even to provide direct answers extracted from that source. Notwithstanding the remarkable progress made and the apparent user preferences for direct answers, this paradigm shift comes at a price which is higher than one might expect at first sight, affecting both users and search engine developers in their own way. We call this tradeoff "the dilemma of the direct answer"; it deserves an analysis which has to go beyond system-oriented aspects but scrutinize the way our society deals with both their information needs and means to information access. The paper in hand contributes to this analysis by putting the evolution of retrieval technology and the expectations at it in the context of information retrieval history. Moreover, we discuss the trade offs in information behavior and information system design that users and developers may face in the future.

scholarly journals Analyzing Web Search Behavior for Software Engineering Tasks

2020 ◽  
Author(s):  
Nikitha Rao ◽  
Chetan Bansal ◽  
Thomas Zimmermann ◽  
Ahmed Hassan Awadallah ◽  
Nachiappan Nagappan
Keyword(s):  
Software Engineering ◽  
Web Search ◽  
Search Behavior
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